[Models] Improve iteration over layers (#26425)

Signed-off-by: Lukas Geiger <lukas.geiger94@gmail.com>

vllm/model_executor/models/apertus.py

@@ -26,6 +26,7 @@
 """Inference-only Apertus model compatible with HuggingFace weights."""
 
 from collections.abc import Iterable
+from itertools import islice
 from typing import Any, Optional, Union
 
 import torch
@@ -412,7 +413,9 @@ class ApertusModel(nn.Module):
             residual = intermediate_tensors["residual"]
 
         aux_hidden_states = []
-        for idx, layer in enumerate(self.layers[self.start_layer : self.end_layer]):
+        for idx, layer in enumerate(
+            islice(self.layers, self.start_layer, self.end_layer)
+        ):
             if idx in self.aux_hidden_state_layers:
                 aux_hidden_states.append(hidden_states + residual)
             hidden_states, residual = layer(positions, hidden_states, residual)

vllm/model_executor/models/falcon_h1.py

@@ -3,6 +3,7 @@
 """Inference-only FalconH1 model."""
 
 from collections.abc import Iterable
+from itertools import islice
 from typing import Optional
 
 import torch
@@ -480,8 +481,7 @@ class FalconH1Model(nn.Module):
             assert intermediate_tensors is not None
             hidden_states = intermediate_tensors["hidden_states"]
 
-        for i in range(self.start_layer, self.end_layer):
-            layer = self.layers[i]
+        for layer in islice(self.layers, self.start_layer, self.end_layer):
             hidden_states = layer(
                 positions=positions,
                 hidden_states=hidden_states,

vllm/model_executor/models/hunyuan_v1.py

@@ -26,6 +26,7 @@
 
 import typing
 from collections.abc import Callable, Iterable
+from itertools import islice
 from typing import Any, Optional, Union
 
 import regex as re
@@ -672,8 +673,9 @@ class HunYuanModel(nn.Module):
 
         cla_factor = _get_cla_factor(self.config)
         prev_kv_states = None
-        for i in range(self.start_layer, self.end_layer):
-            layer = self.layers[i]
+        for i, layer in enumerate(
+            islice(self.layers, self.start_layer, self.end_layer)
+        ):
             hidden_states, residual, kv_states = layer(
                 positions,
                 hidden_states,
@@ -681,10 +683,7 @@ class HunYuanModel(nn.Module):
                 prev_kv_states,
             )
 
-            if (
-                getattr(self.config, "use_cla", False)
-                and (i - self.start_layer) % cla_factor == 0
-            ):
+            if getattr(self.config, "use_cla", False) and i % cla_factor == 0:
                 prev_kv_states = kv_states
             else:
                 prev_kv_states = None

vllm/model_executor/models/lfm2_moe.py

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from collections.abc import Iterable
+from itertools import islice
 from typing import Any, Optional
 
 import torch
@@ -492,7 +493,7 @@ class Lfm2MoeModel(nn.Module):
             hidden_states = intermediate_tensors["hidden_states"]
             residual = intermediate_tensors["residual"]
 
-        for layer in self.layers[self.start_layer : self.end_layer]:
+        for layer in islice(self.layers, self.start_layer, self.end_layer):
             hidden_states, residual = layer(
                 positions=positions,
                 hidden_states=hidden_states,

vllm/model_executor/models/longcat_flash.py

@@ -35,6 +35,7 @@
 
 import typing
 from collections.abc import Callable, Iterable
+from itertools import islice
 from typing import Optional, Union
 
 import torch
@@ -519,8 +520,7 @@ class FlashModel(nn.Module):
             hidden_states = intermediate_tensors["hidden_states"]
             residual = intermediate_tensors["residual"]
 
-        for i in range(self.start_layer, self.end_layer):
-            layer = self.layers[i]
+        for layer in islice(self.layers, self.start_layer, self.end_layer):
             hidden_states, residual = layer(
                 positions,
                 hidden_states,

vllm/model_executor/models/mamba.py

@@ -3,6 +3,7 @@
 """PyTorch MAMBA model."""
 
 from collections.abc import Iterable
+from itertools import islice
 from typing import Optional
 
 import torch
@@ -162,8 +163,7 @@ class MambaModel(nn.Module):
             hidden_states = intermediate_tensors["hidden_states"]
             residual = intermediate_tensors["residual"]
 
-        for i in range(self.start_layer, self.end_layer):
-            layer = self.layers[i]
+        for layer in islice(self.layers, self.start_layer, self.end_layer):
             hidden_states, residual = layer(
                 positions=positions, hidden_states=hidden_states, residual=residual
             )

vllm/model_executor/models/qwen3_vl.py

@@ -26,6 +26,7 @@
 
 from collections.abc import Iterable, Mapping, Sequence
 from functools import partial
+from itertools import islice
 from typing import Any, Callable, Optional, Union
 
 import numpy as np
@@ -1106,11 +1107,9 @@ class Qwen3LLMModel(Qwen3Model):
             assert intermediate_tensors is not None
             hidden_states = intermediate_tensors["hidden_states"]
             residual = intermediate_tensors["residual"]
-        for layer_idx, layer in enumerate(
-            self.layers[self.start_layer : self.end_layer]
+        for layer_idx, layer in islice(
+            enumerate(self.layers), self.start_layer, self.end_layer
         ):
-            layer_idx = layer_idx + self.start_layer
-
             hidden_states, residual = layer(
                 positions,
                 hidden_states,

vllm/model_executor/models/qwen3_vl_moe.py

@@ -26,6 +26,7 @@
 
 import typing
 from collections.abc import Iterable
+from itertools import islice
 from typing import Callable, Optional, Union
 
 import torch
@@ -103,11 +104,9 @@ class Qwen3MoeLLMModel(Qwen3MoeModel):
             assert intermediate_tensors is not None
             hidden_states = intermediate_tensors["hidden_states"]
             residual = intermediate_tensors["residual"]
-        for layer_idx, layer in enumerate(
-            self.layers[self.start_layer : self.end_layer]
+        for layer_idx, layer in islice(
+            enumerate(self.layers), self.start_layer, self.end_layer
         ):
-            layer_idx = layer_idx + self.start_layer
-
             hidden_states, residual = layer(
                 positions,
                 hidden_states,